@inproceedings{cao-etal-2025-detecting,
    title = "Detecting Hallucinations in Scientific Claims by Combining Prompting Strategies and Internal State Classification",
    author = "Cao, Yupeng  and
      Yu, Chun-Nam  and
      Subbalakshmi, K.p.",
    editor = "Ghosal, Tirthankar  and
      Mayr, Philipp  and
      Singh, Amanpreet  and
      Naik, Aakanksha  and
      Rehm, Georg  and
      Freitag, Dayne  and
      Li, Dan  and
      Schimmler, Sonja  and
      De Waard, Anita",
    booktitle = "Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.sdp-1.30/",
    doi = "10.18653/v1/2025.sdp-1.30",
    pages = "316--327",
    ISBN = "979-8-89176-265-7",
    abstract = "Large Language Model (LLM){--}based research assistant tools demonstrate impressive capabilities, yet their outputs may contain hallucinations that compromise reliability. Therefore, detecting hallucinations in automatically generated scientific content is essential. SciHal2025: Hallucination Detection for Scientific Content challenge @ ACL 2025 provides a valuable platform for advancing this goal. This paper presents our solution to the SciHal2025 challenge. Our method combines several prompting strategies with the fine-tuned base LLMs. We first benchmark multiple LLMs on the SciHal dataset. Next, we developed a detection pipeline that integrates few-shot and chain-of-thought prompting. Hidden representations extracted from the LLMs serve as features for an auxiliary classifier, further improving accuracy. Finally, we fine-tuned the selected base LLMs to enhance end-to-end performance. In this paper, we present comprehensive experimental results and discuss the implications of our findings for future hallucination detection research for scientific content."
}